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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
A classification model for predicting course outcomes using ensemble methods Al-Momani, Emad; Shatnawi, Ala'a; Almomani, Mohammad; Almomani, Ammar; Alauthman, Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7090-7102

Abstract

Educational data mining has sparked a lot of attention in latest years. Many machine learning methods have been suggested to discover hidden information from educational data. The extracted knowledge assists institutions in enhancing the effectiveness of teaching tactics and the quality of education. As a result, it improves students' performance and educational outputs overall. In this paper, a classification model was built to classify students' grades in a specific course into different categories (binary and multi-level classification tasks). The dataset contains features related to academic and non-academic information. The models were built using a variety of machine learning algorithms: decision tree (J48), support vector machine (SVM), and k-nearest neighbor (K-NN). Furthermore, ensemble methods (bagging, boosting, random subspace, and random forest) which combined multiple decision tree classifiers were implemented to improve the models' performance. The data set was modified under two stages: features selection method and data augmentation using a method called synthetic minority over sampling technique (SMOTE). Based on the results of the experiments, it is possible to predict the students' performance successfully by using machine learning algorithms and ensemble methods. Random subspace obtained the best accuracy at two-level classification task with modified data with 91.20%. At the three-level classification task, the best accuracy was obtained by random forest with 87.18%.
Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Solikhun, Solikhun; Windarto, Agus Perdana; Alkhairi, Putrama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6466-6477

Abstract

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
The effect of distance learning on student learning achievement: a meta-analysis Nusantara, Bayuk; Hadi, Samsul; Retnawati, Heri; Sumaryanto, Sumaryanto; Prasojo, Lantip Diat; Sotlikova, Rimajon; Arlinwibowo, Janu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6486-6497

Abstract

Distance learning has been an option during the last few years due to the coronavirus disease 2019 (COVID-19) pandemic. The aim of this research is to identify the effectiveness of distance learning compared to conventional learning in terms of student learning achievement. This research is meta- analysis of random group contrast design (experiment-control) models. The data selection process refers to the inclusion criteria of year, theme, data type and data completeness. Based on these criteria, 10 articles were selected. The analysis process begins with testing the homogeneity assumption using three methods, namely ????2, ????2, and Q which shows heterogeneous data so that random model selection is appropriate, testing freedom of publication bias with Egger's test and funnel plot which shows that the data collected is free from publication bias, identifying the effect size and standard error, as well as conducting moderator variable analysis which considers domain, continent, subject, education level and year variables. The results of this study show that although distance learning has a positive influence on student learning achievement, the difference is not significant when compared with conventional learning. In addition, these results can be moderated by achievement domain variables, type of subject, level of education, and year.
A comparative study of machine learning tools for detecting Trojan horse infections in cloud computing environments Kanaker, Hasan; Tarawneh, Monther; Karim, Nader Abdel; Alsaaidah, Adeeb; Abuhamdeh, Maher; Qtaish, Osama; Alhroob, Essam; Alhalhouli, Zaid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6642-6655

Abstract

Cloud computing offers several advantages, including cost savings and easy access to resources, it is also could be vulnerable to serious security attacks such as cloud Trojan horse infection attacks. To address this issue, machine learning is a promising approach for detecting these threats. Thus, different machine learning tools and models have been employed to detect Trojan horse infection such as Weka and Python Colab. This study aims to compare the performance of Weka and Python Colab, as popular tools for building machine learning models. This study evaluates the recall, accuracy, and F1-score of machine learning models built with Weka and Python Colab and compares their computational resources required employing several machine learning algorithms. The dataset collected and analyzed using dynamic analysis of Trojan horse infection in control lab environment. The findings of this study can help determine the decision about which tool to use to detect Trojan horse infections and provide insights into the strengths and limitations of Weka and Python Colab for building machine-learning models in general.
Hybrid digital and analog beamforming design using genetic algorithms Bahri, Sidi Mohammed; Bouacha, Abdelhafid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6389-6400

Abstract

Hybrid analog and digital beamforming is gaining attention for its practical application in large-scale antenna systems. It offers significant cost savings, reduced complexity, and lower power consumption compared to entirely digital beamforming, all while maintaining comparable performance. This article proposes a hybrid beamforming architecture aimed at addressing these challenges by using a reduced number of radio frequency (RF) chains while achieving performance comparable to entirely digital schemes. The study demonstrates that matching the number of RF chains to the total number of data streams enables hybrid beamforming to compete effectively with entirely digital beamformers. The adopted approach focuses on computing analog and digital precoders and combiners using the meta- heuristic method of genetic algorithms, in a point-to-point multiple input multiple output (MIMO) system scenario. The objective is to simplify the system and reduce costs by optimizing the number of antennas, RF chains, and data streams, all while maintaining comparable performance to entirely digital beamforming. The study's results show that increasing the number of antennas significantly impacts the quality and capacity of the hybrid massive MIMO beamforming system. Conversely, reducing the number of RF chains has a negligible effect on quality and capacity, but simplifies the design and minimizes costs.
Hybrid metaheuristic algorithms: a recent comprehensive review with bibliometric analysis Nassef, Ahmed M.; Abdelkareem, Mohammad Ali; Maghrabie, Hussein M.; Baroutaji, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7022-7035

Abstract

Metaheuristic algorithms are widely used in various applications. Collaborating two or more algorithms in a hybrid form has shown great improvements in terms of the algorithm's performance. This paper highlights the recently published work during the last decade from a quantitative perspective. The biometric measures include the number of publications, citations, average citations per publication, h-index, and field-weighted citation impact (FWCI) based on the data extracted from the Scopus database. Statistical measures, co-occurrence and co-authorship maps, and illustrative graphs have been implemented using software tools. According to the collected data, about 3469 articles have been published during the last decade with an increasing rate of 44.1 papers per year. Most of these articles have been published as journal articles with a percentage of 68.3%, followed by conference articles occupied 29.5%. China, India and Iran contributed the largest number of articles at 1076, 965, and 239, respectively. Parouha, Verma, and Kamel, are the top-ranked authors with 14, 10, and 9 publications, respectively. The most areas of interest are computer science, engineering and mathematics with publication percentages of 27.69%, 25.55% and 13.91%, respectively. The data presented in this paper gives the researchers a clear image of this hot topic to start new research.
Noise reduction in Hyperion high dynamic range hyperspectral data using machine learning and statistical techniques Nair, Priyanka; Srivastava, Devesh Kumar; Bhatnagar, Roheet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6913-6928

Abstract

Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original imagery's complete dynamic range. The study explores various noise reduction methods, such as minimum noise fraction (MNF), principal component analysis (PCA), wavelet denoising, non-local means (NLM), and denoising autoencoders, aimed at enhancing the signal-to-noise ratio. The effectiveness of these techniques is evaluated through visual quality, mean square error (MSE), and peak signal-to-noise ratio (PSNR), alongside their impact on mineral exploration. Furthermore, the paper investigates the application of machine learning algorithms on denoised data for mineral identification, highlighting the potential of integrating denoising techniques with machine learning for improved mineral exploration. This comparative analysis aims to identify the most efficient noise removal methods for hyperspectral imagery, facilitating higher quality data for subsequent analysis.
A performance evaluation of the internet of things-message queue telemetry transport protocol based water level warning system Sonklin, Kachane; Sonklin, Chanipa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7178-7185

Abstract

The internet of things (IoT) and message queue telemetry transport (MQTT) play crucial roles in connecting sensor networks, data exchange among diverse devices, and enabling various smart systems. Several studies have been conducted on IoT-MQTT-based applications because of their ease of implementation and deployment. It also offers real-time and reliable communication between a publisher and a subscriber. However, there is a lack of comprehensive studies covering overall performance metrics. Therefore, this paper aims to develop a water level warning system prototype and evaluate its performance through simulation experiments, focusing on critical metrics, such as latency, throughput, packet loss rate (PLR), packet delivery ratio (PDR), and availability at various data transmission rates. The results demonstrate that the proposed system achieves significantly lower latency, compared to existing solutions and achieves up to 98% availability and reliability with minimal packet loss. The experimental findings also reveal that higher data transmission rates lead to higher throughput and latency performance with lower performance in terms of availability, PDR, and sensor accuracy.
A combined control method of supply harmonic current and source harmonic voltage for series hybrid active power filter Thuyen, Chau Minh; Nguyen, Hoai Thuong; Thao, Phan Thi Bich
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6057-6065

Abstract

The series hybrid active power filter (SHAPF) is known as a very effective harmonic filtering model in power systems. Typically, SHAPF is controlled by a control method based either on the harmonic voltage of the load or on the supply harmonic current. However, the above two methods have the disadvantage of requiring the control coefficient much be large enough, which easily causes system instability. Therefore, this paper presents a new control method for SHAPF. It is a combined control method of the supply harmonic current and the source harmonic voltage. The advantage of the proposed method is the ability to reduce the total harmonic distortion of the supply current and voltage applied to the load with a control coefficient that is not too large. A fuzzy-proportional integral controller is designed for the proposed control method to reduce the compensation error in steady state under variable load conditions. Mathematical models and simulation results have demonstrated the effectiveness of the proposed method in reducing the total harmonic distortion of the supply current, voltage applied to the load and minimize the compensation error at steady state.
Efficient smart distributed face identification using the MixMaxSim decision function Ahmadi, Sayed Mohammad; Dianat, Rouhollah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7145-7157

Abstract

Recognizing a large number of people is a common challenge in face identification applications, involving decreased accuracy, increased memory and time complexities. To address these issues, this study introduces a three-module approach: “toilers,” “affinity-meter,” and “decision-maker.” Unlike the random distribution methods used in previous solutions, this method employs clustering to distribute the problem into subnetworks called “toilers.” The toiler’s module calculates the likelihood of test data belonging to each class of each toiler, using the last layer outputs of deep learning models. Meanwhile, the affinity-meter module determines the similarity between the test data and the average of each class, employing a similarity measure. The decision-maker module combines the reports from the previous two modules and selects the final class, utilizing a mix of the max-max criterion and the similarity criterion. The proposed method outperforms existing solutions, achieving improved recall, precision, and F1-score. It effectively addresses memory, speed, and accuracy issues in face identification, surpassing both no-distribution and random methods on Glint360K, VGGFace2, and MS-Celeb-1M datasets. Overall, this method offers a more efficient and accurate approach by distributing the problem into subnetworks, demonstrating superior performance and scalability for large-scale face recognition applications.

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